In recent years, various techniques for detecting an object such as a human face from an image have been proposed. For example, the following object detection technique has been proposed. A template created from a reference image is overlaid on an object search target image, and is scanned. At respective overlaying positions, distances (or similar values or similarities) between the object search target image and template (or between feature amounts of the object search target image and those of the template) are computed. Subsequently, an overlaying position where a minimum distance is obtained (or an overlaying position where a maximum similarity between the object search target image and template is obtained) is output as an object detection position.
In order to accurately search for an object, for example, a template is shifted pixel by pixel with respect to an object search target image, and a similarity between the object search target image and template is computed in correspondence with each shift position.
However, with the above object search method, the computation load for an object search is heavy, resulting in high computing cost. That is, an object search load is heavy. Hence, an efficiency enhancement technique of an object search is demanded.